饲料成本是水貂生产系统的最大支出,and,因此,通过选择高饲料效率的水貂来提高饲料效率(FE)是提高水貂产业可持续性的实用方法。在这项研究中,我们估计了遗传力,表型,以及不同FE度量和成分性状的遗传相关性,包括收获重量(HW),收获长度(HL),最终主体长度(FBL),最终体重(FBW),平均日增益(ADG),日采食量(DFI),饲料转化率(FCR),残余饲料摄入量(RFI),剩余增益(RG),剩余进气和增益(RIG),和克莱伯比率(KR),使用来自2,288美国水貂(HW和HL)的数据,和1,038至1,906美国水貂(其他性状)。固定效应的显著性(P<0.05)(农场,性别,和颜色类型),协变量(动物的年龄),和随机效应(加性遗传,母性,和普通垃圾)通过ASReml-R版本4中实施的单变量模型进行评估。通过使用ASReml-R版本4拟合一组双变量模型来估计遗传参数。HW的遗传力(±SE)估计值为0.28±0.06、0.23±0.06、0.28±0.10、0.27±0.11、0.25±0.09、0.26±0.09、0.20±0.09、0.23±0.09、0.21±0.10、0.25±0.10和0.26±0.10,HL,FBL,FBW,ADG,DFI,FCR,RFI,RG,RIG,还有KR,分别。RIG与DFI(-0.62±0.24)和ADG(0.58±0.21)具有良好的遗传相关性,与FBW(0.14±0.31)和FBL(-0.15±0.31)的遗传相关性无显着(P>0.05)。这些结果表明,RIG可能是一种优异的性状,因为它可以保证降低饲料摄入量,同时加快水貂的生长速度,但对体重和长度没有负面影响。此外,KR与组成性状之间的强正相关(±SE)(与FBW为0.88±0.11,0.68±0.17与FBL,和ADG的0.97±0.02)建议KR作为FE的一种适用的间接测量,以改善成分性状,因为它不需要测量单个的采食量。总的来说,我们的结果证实了在水貂育种计划中包括FE性状以有效选择饲料有效动物的可能性。
提高美国水貂的饲料效率(FE)是非常有益的,因为饲料成本是水貂生产系统的最大费用。本研究估计了遗传力,水貂不同FE度量和组分性状的表型和遗传相关性。剩余的摄入量和增益可以作为FE测量应用在选择程序,因为它将保证更快的增长与减少饲料摄入量的水貂,但对生长性状没有负面影响。此外,Kleiber比率与组分性状具有很强的正相关,这使得这个性状成为水貂育种项目中吸引人的间接FE性状,知道这一特性不依赖于采食记录。总的来说,我们的结果表明,包括FE性状可以帮助水貂育种计划制定选择饲料有效水貂的指标,因此,降低水貂生产成本。
Feed cost is the largest expense of mink production systems, and, therefore, improvement of feed efficiency (FE) through selection for high feed-efficient mink is a practical way to increase the mink industry\'s sustainability. In this study, we estimated the heritability, phenotypic, and genetic correlations for different FE measures and component traits, including harvest weight (HW), harvest length (HL), final body length (FBL), final body weight (FBW), average daily gain (ADG), daily feed intake (DFI), feed conversion ratio (FCR), residual feed intake (RFI), residual gain (RG), residual intake and gain (RIG), and Kleiber ratio (KR), using data from 2,288 American mink (for HW and HL), and 1,038 to 1,906 American mink (for other traits). Significance (P < 0.05) of fixed effects (farm, sex, and color type), a covariate (age of animal), and random effects (additive genetic, maternal, and common litter) were evaluated through univariate models implemented in ASReml-R version 4. Genetic parameters were estimated via fitting a set of bivariate models using ASReml-R version 4. Estimates of heritabilities (±SE) were 0.28 ± 0.06, 0.23 ± 0.06, 0.28 ± 0.10, 0.27 ± 0.11, 0.25 ± 0.09, 0.26 ± 0.09, 0.20 ± 0.09, 0.23 ± 0.09, 0.21 ± 0.10, 0.25 ± 0.10, and 0.26 ± 0.10 for HW, HL, FBL, FBW, ADG, DFI, FCR, RFI, RG, RIG, and KR, respectively. RIG had favorable genetic correlations with DFI (-0.62 ± 0.24) and ADG (0.58 ± 0.21), and nonsignificant (P > 0.05) genetic correlations with FBW (0.14 ± 0.31) and FBL (-0.15 ± 0.31). These results revealed that RIG might be a superior trait as it guarantees reduced feed intake with faster-growing mink yet with no negative impacts on body weight and length. In addition, the strong positive genetic correlations (±SE) between KR with component traits (0.88 ± 0.11 with FBW, 0.68 ± 0.17 with FBL, and 0.97 ± 0.02 with ADG) suggested KR as an applicable indirect measure of FE for improvement of component traits as it did not require the individual feed intake to be measured. Overall, our results confirmed the possibility of including FE traits in mink breeding programs to effectively select feed-efficient animals.
Improvement of feed efficiency (FE) in American mink is highly beneficial, as feed costs comprise the largest expense of mink production systems. The present study estimated the heritability, phenotypic and genetic correlations for different FE measures and component traits in mink. The residual intake and gain can be applied as FE measurement in selection programs as it will guarantee faster-growing mink with reduced feed intake, yet without negative impacts on growth traits. In addition, Kleiber ratio had strong positive genetic correlations with component traits, which made this trait an appealing indirect FE trait for mink breeding programs, knowing the fact that this trait was not dependent on feed intake records. Overall, our results suggested that including FE traits can assist mink breeding programs to develop an index for the selection of feed-efficient mink and, therefore, reduce the cost of mink production.